OpenRaiser/Intern-Atlas huggingface dataset:
Conveyor: Efficient Tool-aware LLM Serving with Tool Partial Execution
Venue: arXiv cs.CL 2024
Year: 2024

The complexity of large language model (LLM) serving workloads has substantially increased due to the integration with external tool invocations, such as ChatGPT plugins. In this paper, we identify a new opportunity for efficient LLM serving for requests that trigger tools: tool partial execution alongside LLM decoding. To this end, we design Conveyor, an efficient LLM serving system optimized for handling requests involving external tools. We introduce a novel interface for tool developers to expose partial execution opportunities to the LLM serving system and a request scheduler that facilitates partial tool execution. Our results demonstrate that tool partial execution can improve request completion latency by up to 38.8%.
A Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning
Venue: arXiv cs.LG 2024
Year: 2024

Machine learning models have recently enjoyed a significant increase in size and popularity. However, this growth has created concerns about dataset privacy. To counteract data leakage, various privacy frameworks guarantee that the output of machine learning models does not compromise their training data. However, this privatization comes at a cost by adding random noise to the training process, which reduces model performance. By making models more resistant to small changes in input and thus more stable, the necessary amount of noise can be decreased while still protecting privacy. This paper investigates various techniques to enhance stability, thereby minimizing the negative effects of privatization in machine learning.
Arbitrary-Length Generalization for Addition in a Tiny Transformer
Venue: arXiv cs.LG 2024
Year: 2024

This paper introduces a novel training methodology that enables a Transformer model to generalize the addition of two-digit numbers to numbers with unseen lengths of digits. The proposed approach employs an autoregressive generation technique, processing from right to left, which mimics a common manual method for adding large numbers. To the best of my knowledge, this methodology has not been previously explored in the literature. All results are reproducible, and the corresponding R code is available at github.com/AGPatriota/ALGA-R/.
Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data
Venue: arXiv cs.CV 2024
Year: 2024

Recent years have witnessed remarkable progress in multi-view diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical bottleneck is the scarcity of high-quality 3D objects with detailed captions. To address this challenge, we propose Bootstrap3D, a novel framework that automatically generates an arbitrary quantity of multi-view images to assist in training multi-view diffusion models. Specifically, we introduce a data generation pipeline that employs (1) 2D and video diffusion models to generate multi-view images based on constructed text prompts, and (2) our fine-tuned 3D-aware MV-LLaVA for filtering high-quality data and rewriting inaccurate captions. Leveraging this pipeline, we have generated 1 million high-quality synthetic multi-view images with dense descriptive captions to address the shortage of high-quality 3D data. Furthermore, we present a Training Timestep Reschedule (TTR) strategy that leverages the denoising process to learn multi-view consistency while maintaining the original 2D diffusion prior. Extensive experiments demonstrate that Bootstrap3D can generate high-quality multi-view images with superior aesthetic quality, image-text alignment, and maintained view consistency.
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning
Venue: arXiv cs.HC 2024
Year: 2024

Explanations of an AI's function can assist human decision-makers, but the most useful explanation depends on the decision's context, referred to as the downstream task. User studies are necessary to determine the best explanations for each task. Unfortunately, testing every explanation and task combination is impractical, especially considering the many factors influencing human+AI collaboration beyond the explanation's content. This work leverages two insights to streamline finding the most effective explanation. First, explanations can be characterized by properties, such as faithfulness or complexity, which indicate if they contain the right information for the task. Second, we introduce XAIsim2real, a pipeline for running synthetic user studies. In our validation study, XAIsim2real accurately predicts user preferences across three tasks, making it a valuable tool for refining explanation choices before full studies. Additionally, it uncovers nuanced relationships, like how cognitive budget limits a user's engagement with complex explanations -- a trend confirmed with real users.
ADEP: A Novel Approach Based on Discriminator-Enhanced Encoder-Decoder Architecture for Accurate Prediction of Adverse Effects in Polypharmacy
Venue: arXiv cs.LG 2024
Year: 2024

Motivation: Unanticipated drug-drug interactions (DDIs) pose significant risks in polypharmacy, emphasizing the need for predictive methods. Recent advancements in computational techniques aim to address this challenge. Methods: We introduce ADEP, a novel approach integrating a discriminator and an encoder-decoder model to address data sparsity and enhance feature extraction. ADEP employs a three-part model, including multiple classification methods, to predict adverse effects in polypharmacy. Results: Evaluation on benchmark datasets shows ADEP outperforms well-known methods such as GGI-DDI, SSF-DDI, LSFC, DPSP, GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, Random Forest, K-Nearest-Neighbor, Logistic Regression, and Decision Tree. Key metrics include Accuracy, AUROC, AUPRC, F-score, Recall, Precision, False Negatives, and False Positives. ADEP achieves more accurate predictions of adverse effects in polypharmacy. A case study with real-world data illustrates ADEP's practical application in identifying potential DDIs and preventing adverse effects. Conclusions: ADEP significantly advances the prediction of polypharmacy adverse effects, offering improved accuracy and reliability. Its innovative architecture enhances feature extraction from sparse medical data, improving medication safety and patient outcomes. Availability: Source code and datasets are available at https://github.com/m0hssn/ADEP.
Distributed Ranges: A Model for Distributed Data Structures, Algorithms, and Views
Venue: arXiv cs.DC 2024
Year: 2024

Data structures and algorithms are essential building blocks for programs, and \emph{distributed data structures}, which automatically partition data across multiple memory locales, are essential to writing high-level parallel programs. While many projects have designed and implemented C++ distributed data structures and algorithms, there has not been widespread adoption of an interoperable model allowing algorithms and data structures from different libraries to work together. This paper introduces distributed ranges, which is a model for building generic data structures, views, and algorithms. A distributed range extends a C++ range, which is an iterable sequence of values, with a concept of segmentation, thus exposing how the distributed range is partitioned over multiple memory locales. Distributed data structures provide this distributed range interface, which allows them to be used with a collection of generic algorithms implemented using the distributed range interface. The modular nature of the model allows for the straightforward implementation of \textit{distributed views}, which are lightweight objects that provide a lazily evaluated view of another range. Views can be composed together recursively and combined with algorithms to implement computational kernels using efficient, flexible, and high-level standard C++ primitives. We evaluate the distributed ranges model by implementing a set of standard concepts and views as well as two execution runtimes, a multi-node, MPI-based runtime and a single-process, multi-GPU runtime. We demonstrate that high-level algorithms implemented using generic, high-level distributed ranges can achieve performance competitive with highly-tuned, expert-written code.
Flexible and Efficient Surrogate Gradient Modeling with Forward Gradient Injection
Venue: arXiv cs.LG 2024
Year: 2024

Automatic differentiation is a key feature of present deep learning frameworks. Moreover, they typically provide various ways to specify custom gradients within the computation graph, which is of particular importance for defining surrogate gradients in the realms of non-differentiable operations such as the Heaviside function in spiking neural networks (SNNs). PyTorch, for example, allows the custom specification of the backward pass of an operation by overriding its backward method. Other frameworks provide comparable options. While these methods are common practice and usually work well, they also have several disadvantages such as limited flexibility, additional source code overhead, poor usability, or a potentially strong negative impact on the effectiveness of automatic model optimization procedures. In this paper, an alternative way to formulate surrogate gradients is presented, namely, forward gradient injection (FGI). FGI applies a simple but effective combination of basic standard operations to inject an arbitrary gradient shape into the computational graph directly within the forward pass. It is demonstrated that using FGI is straightforward and convenient. Moreover, it is shown that FGI can significantly increase the model performance in comparison to custom backward methods in SNNs when using TorchScript. These results are complemented with a general performance study on recurrent SNNs with TorchScript and torch.compile, revealing the potential for a training speedup of more than 7x and an inference speedup of more than 16x in comparison with pure PyTorch.
Kinematic Model of Magnetic Domain Wall Motion for Fast, High-Accuracy Simulations
Venue: arXiv cs.ET 2024
Year: 2024

Domain wall (DW) devices have garnered recent interest for diverse applications including memory, logic, and neuromorphic primitives; fast, accurate device models are therefore imperative for large-scale system design and verification. Extant DW motion models are sub-optimal for large-scale system design either over-consuming compute resources with physics-heavy equations or oversimplifying the physics, drastically reducing model accuracy. We propose a DW model inspired by the phenomenological similarities between motions of a DW and a classical object being acted on by forces like air resistance or static friction. Our proposed phenomenological model predicts DW motion within 1.2% on average compared with micromagnetic simulations that are 400 times slower. Additionally our model is seven times faster than extant collective coordinate models and 14 times more accurate than extant hyper-reduced models making it an essential tool for large-scale DW circuit design and simulation. The model is publicly posted along with scripts that automatically extract model parameters from user-provided simulation or experimental data to extend the model to alternative micromagnetic parameters.
LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking
Venue: arXiv cs.IR 2024
Year: 2024

Ranking passages by prompting a large language model (LLM) can achieve promising performance in modern information retrieval (IR) systems. A common approach to sort the ranking list is by prompting LLMs for a pairwise or setwise comparison which often relies on sorting algorithms. However, sorting-based methods require consistent comparisons to correctly sort the passages, which we show that LLMs often violate. We identify two kinds of intrinsic inconsistency in LLM-based pairwise comparisons: order inconsistency which leads to conflicting results when switching the passage order, and transitive inconsistency which leads to non-transitive triads among all preference pairs. Our study of these inconsistencies is relevant for understanding and improving the stability of any ranking scheme based on relative preferences. In this paper, we propose LLM-RankFusion, an LLM-based ranking framework that mitigates these inconsistencies and produces a robust ranking list. LLM-RankFusion mitigates order inconsistency using in-context learning (ICL) to demonstrate order-agnostic comparisons and calibration to estimate the underlying preference probability between two passages. We then address transitive inconsistency by aggregating the ranking results from multiple rankers. In our experiments, we empirically show that LLM-RankFusion can significantly reduce inconsistent comparison results, improving the ranking quality by making the final ranking list more robust. Our code is available at \href{https://github.com/XHMY/LLM-RankFusion}{https://github.com/XHMY/LLM-RankFusion}
Controlling Large Language Model Agents with Entropic Activation Steering
Venue: arXiv cs.CL 2024
Year: 2024

The rise of large language models (LLMs) has prompted increasing interest in their use as in-context learning agents. At the core of agentic behavior is the capacity for exploration, or the ability to actively gather information about the environment. But how do LLM agents explore, and how can we control their exploratory behaviors? To answer these questions, we take a representation-level perspective, and introduce Entropic Activation Steering (EAST), an activation steering method for in-context LLM agents. Firstly, we demonstrate that EAST can effectively manipulate an LLM agent's exploration by directly affecting the high-level actions parsed from the outputs of the LLM, in contrast to token-level temperature sampling. Secondly, we reveal how applying this control modulates the uncertainty exhibited in the LLM's thoughts, guiding the agent towards more exploratory actions. Finally, we demonstrate that the steering vectors obtained by EAST generalize across task variants. In total, these results show that LLM agents explicitly encode uncertainty over their actions in their representation space. Our work paves the way for a new understanding of the functioning of LLM agents and to effective control of their decision-making behaviors.
Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs
Venue: arXiv cs.CL 2024
Year: 2024

Natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. To facilitate chart-based reasoning using natural language, various downstream tasks have been introduced recently such as chart question answering, chart summarization, and fact-checking with charts. These tasks pose a unique challenge, demanding both vision-language reasoning and a nuanced understanding of chart data tables, visual encodings, and natural language prompts. Despite the recent success of Large Language Models (LLMs) across diverse NLP tasks, their abilities and limitations in the realm of data visualization remain under-explored, possibly due to their lack of multi-modal capabilities. To bridge the gap, this paper presents the first comprehensive evaluation of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks. Our evaluation includes a comprehensive assessment of LVLMs, including GPT-4V and Gemini, across four major chart reasoning tasks. Furthermore, we perform a qualitative evaluation of LVLMs' performance on a diverse range of charts, aiming to provide a thorough analysis of their strengths and weaknesses. Our findings reveal that LVLMs demonstrate impressive abilities in generating fluent texts covering high-level data insights while also encountering common problems like hallucinations, factual errors, and data bias. We highlight the key strengths and limitations of chart comprehension tasks, offering insights for future research.
Adversarial 3D Virtual Patches using Integrated Gradients
Venue: arXiv cs.CV 2024
Year: 2024

LiDAR sensors are widely used in autonomous vehicles to better perceive the environment. However, prior works have shown that LiDAR signals can be spoofed to hide real objects from 3D object detectors. This study explores the feasibility of reducing the required spoofing area through a novel object-hiding strategy based on virtual patches (VPs). We first manually design VPs (MVPs) and show that VP-focused attacks can achieve similar success rates with prior work but with a fraction of the required spoofing area. Then we design a framework Saliency-LiDAR (SALL), which can identify critical regions for LiDAR objects using Integrated Gradients. VPs crafted on critical regions (CVPs) reduce object detection recall by at least 15% compared to our baseline with an approximate 50% reduction in the spoofing area for vehicles of average size.
Neural Optimal Transport with Lagrangian Costs
Venue: arXiv cs.LG 2024
Year: 2024

We investigate the optimal transport problem between probability measures when the underlying cost function is understood to satisfy a least action principle, also known as a Lagrangian cost. These generalizations are useful when connecting observations from a physical system where the transport dynamics are influenced by the geometry of the system, such as obstacles (e.g., incorporating barrier functions in the Lagrangian), and allows practitioners to incorporate a priori knowledge of the underlying system such as non-Euclidean geometries (e.g., paths must be circular). Our contributions are of computational interest, where we demonstrate the ability to efficiently compute geodesics and amortize spline-based paths, which has not been done before, even in low dimensional problems. Unlike prior work, we also output the resulting Lagrangian optimal transport map without requiring an ODE solver. We demonstrate the effectiveness of our formulation on low-dimensional examples taken from prior work. The source code to reproduce our experiments is available at https://github.com/facebookresearch/lagrangian-ot.
Benchmarking for Deep Uplift Modeling in Online Marketing
Venue: arXiv cs.LG 2024
Year: 2024

Online marketing is critical for many industrial platforms and business applications, aiming to increase user engagement and platform revenue by identifying corresponding delivery-sensitive groups for specific incentives, such as coupons and bonuses. As the scale and complexity of features in industrial scenarios increase, deep uplift modeling (DUM) as a promising technique has attracted increased research from academia and industry, resulting in various predictive models. However, current DUM still lacks some standardized benchmarks and unified evaluation protocols, which limit the reproducibility of experimental results in existing studies and the practical value and potential impact in this direction. In this paper, we provide an open benchmark for DUM and present comparison results of existing models in a reproducible and uniform manner. To this end, we conduct extensive experiments on two representative industrial datasets with different preprocessing settings to re-evaluate 13 existing models. Surprisingly, our experimental results show that the most recent work differs less than expected from traditional work in many cases. In addition, our experiments also reveal the limitations of DUM in generalization, especially for different preprocessing and test distributions. Our benchmarking work allows researchers to evaluate the performance of new models quickly but also reasonably demonstrates fair comparison results with existing models. It also gives practitioners valuable insights into often overlooked considerations when deploying DUM. We will make this benchmarking library, evaluation protocol, and experimental setup available on GitHub.
An Effective Weight Initialization Method for Deep Learning: Application to Satellite Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

The growing interest in satellite imagery has triggered the need for efficient mechanisms to extract valuable information from these vast data sources, providing deeper insights. Even though deep learning has shown significant progress in satellite image classification. Nevertheless, in the literature, only a few results can be found on weight initialization techniques. These techniques traditionally involve initializing the networks' weights before training on extensive datasets, distinct from fine-tuning the weights of pre-trained networks. In this study, a novel weight initialization method is proposed in the context of satellite image classification. The proposed weight initialization method is mathematically detailed during the forward and backward passes of the convolutional neural network (CNN) model. Extensive experiments are carried out using six real-world datasets. Comparative analyses with existing weight initialization techniques made on various well-known CNN models reveal that the proposed weight initialization technique outperforms the previous competitive techniques in classification accuracy. The complete code of the proposed technique, along with the obtained results, is available at https://github.com/WadiiBoulila/Weight-Initialization
Better coloring of 3-colorable graphs
Venue: arXiv cs.DS 2024
Year: 2024

We consider the problem of coloring a 3-colorable graph in polynomial time using as few colors as possible. This is one of the most challenging problems in graph algorithms. In this paper using Blum's notion of ``progress'', we develop a new combinatorial algorithm for the following: Given any 3-colorable graph with minimum degree $\ds>\sqrt n$, we can, in polynomial time, make progress towards a $k$-coloring for some $k=\sqrt{n/\ds}\cdot n^{o(1)}$. We balance our main result with the best-known semi-definite(SDP) approach which we use for degrees below $n^{0.605073}$. As a result, we show that $\tO(n^{0.19747})$ colors suffice for coloring 3-colorable graphs. This improves on the previous best bound of $\tO(n^{0.19996})$ by Kawarabayashi and Thorup in 2017.
Cognitive Manipulation: Semi-supervised Visual Representation and Classroom-to-real Reinforcement Learning for Assembly in Semi-structured Environments
Venue: arXiv cs.RO 2024
Year: 2024

Assembling a slave object into a fixture-free master object represents a critical challenge in flexible manufacturing. Existing deep reinforcement learning-based methods, while benefiting from visual or operational priors, often struggle with small-batch precise assembly tasks due to their reliance on insufficient priors and high-costed model development. To address these limitations, this paper introduces a cognitive manipulation and learning approach that utilizes skill graphs to integrate learning-based object detection with fine manipulation models into a cohesive modular policy. This approach enables the detection of the master object from both global and local perspectives to accommodate positional uncertainties and variable backgrounds, and parametric residual policy to handle pose error and intricate contact dynamics effectively. Leveraging the skill graph, our method supports knowledge-informed learning of semi-supervised learning for object detection and classroom-to-real reinforcement learning for fine manipulation. Simulation experiments on a gear-assembly task have demonstrated that the skill-graph-enabled coarse-operation planning and visual attention are essential for efficient learning and robust manipulation, showing substantial improvements of 13$\%$ in success rate and 15.4$\%$ in number of completion steps over competing methods. Real-world experiments further validate that our system is highly effective for robotic assembly in semi-structured environments.